LITMIR.BIZ

      Step 1: Gather new data and existing knowledge (Level 1).

      Step 2: Cleanse and structure data.

      Step 3: Create information (Level 2).

      Step 4: Create insights (Level 3).

      Step 5: Deliver to the right end user in the right format, channel, and time.

      Step 6: Decide and take action.

      Step 7: Create knowledge (Level 4).

      Step 8: Share knowledge.

      ● Steps 1 and 2: Functional or geographic silos lead to the creation of disparate data sets. Inconsistent definitions of data structures and elements exist with varying time stamps. Various imperfect and outdated copies of the original sources lead to tens, hundreds, or thousands of manual adjustments and more errors in the data.

      ● Step 3: Too much information means that really interesting signals get lost. There is a lack of a proper hypothesis of what to analyze.

      ● Step 4: There is a lack of thinking and business understanding. Data scientists sometimes do not fully understand the end users' needs. Contextual information is lacking, making interpretation difficult or impossible. Prior knowledge is not applied, either because it does not exist or because it is not accessible in time and at a reasonable cost.

      ● Steps 5 and 6: Communication problems occur between the central data analytics teams and the actual end users. Distribution issues prevent the insights from being delivered – the so-called Last Mile problem is in effect. There is an ineffective packaging and delivery model for the specific needs of the end user.

      ● Steps 7 and 8: There is a lack of accountability for creating and managing the knowledge. Central knowledge management systems contain a lot of obsolete and irrelevant content, and a lack of documentation leads to loss of knowledge (e.g., in cases of employee attrition).

      ● Less but more insightful analysis addressing 100 percent of the analytics use case

      ● Analytic outputs embedded in the normal workflow

      ● More targeted and trigger-based delivery highlighting the key issues rather than just regular reporting or pull analysis

      ● Short, relevant alerts to the end user rather than big reports deposited on some central system

      ● Lower infrastructure cost and overhead allocations to the end users

      ● No requirement to start a major information technology (IT) project to get even simple analyses

      ● Simple, pay-as-you-go models for analytics output rather than significant fixed costs

      ● Full knowledge management of analytics use cases, so that the world does not need to be reinvented each time there is a change